# Stroke Connectome MRI biomarkers for VCID risk assessment

> **NIH NIH R01** · UNIVERSITY OF WISCONSIN-MADISON · 2022 · $736,934

## Abstract

PROJECT SUMMARY
Every year, more than 795,000 people in the United States have a stroke, with currently around 4.7 million
survivors. Approximately 20% of survivors develop vascular contributions to cognitive impairments and dementia
(VCID) which is second only to Alzheimer’s disease (AD). While several putative biomarkers are known, a
considerable gap exists in stroke research in terms of validation and interaction of biomarkers of VCID. There is
a critical need to better understand the complex interactions of VCID risk factors, baseline cognitive and brain
health, and incident stroke lesion burden on post stroke brain changes and subsequent development of VCID.
The specific aims of this project will address this need innovatively by (1) utilizing a novel neighborhood
disadvantage atlas to geo-spatially map and quantify socio-economic disadvantage, (2) quantifying vascular risk
burden, (3) incorporating baseline brain and cognitive health, (4) leveraging technical advances in state-of-the-
art connectome MRI, and (5) applying network neuroscience and machine learning. In addition, we will recruit
participants from underrepresented minority groups (African Americans, Hispanics, Native Americans),
rural/urban, low/high SES who might be at increased risk for VCID. Our central hypothesis is that VCID risk
factors, baseline cognitive and brain health, incident stroke damage, and post stroke brain changes will
act in concert through brain perfusion, structure, and connectivity pathways in determining whether a
stroke patient develops VCID. We will collect longitudinal connectome MRI and Neuropsychological data from
a prospective cohort of patients 55-90 years old with incident ischemic stroke in the left (n=50) or right (n=50)
middle cerebral artery territory. We will prospectively collect data on n=50 and retrospectively use n=100 from
AD connectome project for matched healthy controls. Aim 1 (Brain changes): Characterize how the interaction
of VCID risk factors (e.g., cardiovascular, demographics), baseline brain health and the extent of incident stroke
damage will affect post stroke brain changes at 6 months. Aim 2 (Brain-cognition relationships): Characterize
specific relationships between VCID risk factors, baseline cognition, brain, incident stroke, post stroke brain
changes and post stroke cognitive function at 6 and 12-months across 5 cognitive domains including executive
function, attention, language, memory and visuospatial. We will use advanced machine learning to build
predictive models that will identify contributory and deleterious brain changes associated with post stroke
cognitive outcomes. Successful completion of the project will provide currently lacking scientific understanding
of the intricate biological relationships between VCID risk factors, stroke MRI biomarkers, and their interactions,
that underlie the biology of cognitive outcomes after an ischemic stroke. The results will lay a strong foundation
for building accurate diagn...

## Key facts

- **NIH application ID:** 10444411
- **Project number:** 1R01NS123378-01A1
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Nagesh Adluru
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $736,934
- **Award type:** 1
- **Project period:** 2022-04-01 → 2027-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10444411

## Citation

> US National Institutes of Health, RePORTER application 10444411, Stroke Connectome MRI biomarkers for VCID risk assessment (1R01NS123378-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10444411. Licensed CC0.

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